Semi-supervised learning (SSL) is a popular solution to alleviate the high annotation cost in medical image classification. As a main branch of SSL, consistency regularization engages in imposing consensus between the predictions of a single sample from different views, termed as Absolute Location consistency (AL-c). However, only AL-c may be insufficient. Just like when diagnosing a case in practice, besides the case itself, the doctor usually refers to certain related trustworthy cases to make more reliable decisions.Therefore, we argue that solely relying on AL-c may ignore the relative differences across samples, which we interpret as relative locations, and only exploit limited information from one perspective. To address this issue, we propose a Sample Consistency Mean Teacher (SCMT) which not only incorporates AL c but also additionally enforces consistency between the samples' relative similarities to its related samples, called Relative Location consistency (RL c). AL c and RL c conduct consistency regularization from two different perspectives, jointly extracting more diverse semantic information for classification. On the other hand, due to the highly similar structures in medical images, the sample distribution could be overly dense in feature space, making their relative locations susceptible to noise. To tackle this problem, we further develop a Sample Scatter Mean Teacher (SSMT) by utilizing contrastive learning to sparsify the sample distribution and obtain robust and effective relative locations. Extensive experiments on different datasets demonstrate the superiority of our method.

翻译：半监督学习（SSL）是缓解医学图像分类中高标注成本的一种常用解决方案。作为SSL的主要分支，一致性正则化致力于在不同视角下对同一样本的预测之间施加共识，这被称为绝对位置一致性（AL-c）。然而，仅依赖AL-c可能是不够的。正如在实际诊断病例时，医生除了参考病例本身，通常还会查阅某些相关的可信病例以做出更可靠的决策。因此，我们认为仅依赖AL-c可能会忽略样本间的相对差异（我们将其解释为相对位置），并且仅从一个视角利用有限的信息。为解决这一问题，我们提出了样本一致性均值教师（SCMT），它不仅包含AL-c，还额外强制样本与其相关样本之间的相对相似性保持一致，这被称为相对位置一致性（RL-c）。AL-c和RL-c从两个不同视角进行一致性正则化，共同提取更多样化的语义信息用于分类。另一方面，由于医学图像中结构高度相似，样本分布在特征空间中可能过于密集，使得它们的相对位置容易受到噪声影响。为解决这一问题，我们进一步开发了样本稀疏均值教师（SSMT），通过利用对比学习来稀疏化样本分布，从而获得鲁棒且有效的相对位置。在不同数据集上的大量实验证明了我们方法的优越性。